p-93-03

Learning Adaptation Strategies by Introspective Reasoning about Memory
Search

David B. Leake 

Proceedings of the AAAI-93 Workshop on Case-Based Reasoning, AAAI
Press, Menlo Park, CA, pp. 57-63, 1993.


Abstract:

In case-based reasoning systems, the case adaptation process is
traditionally controlled by static libraries of hand-coded adaptation
rules.  This paper proposes a method for learning adaptation knowledge
in the form of _adaptation strategies_ of the type developed and
hand-coded by Kass [90].  Adaptation strategies differ from standard
adaptation rules in that they encode general memory search procedures
for finding the information needed during case adaptation; this paper
focuses on the issues involved in learning memory search procedures to
form the basis of new adaptation strategies.  It proposes a method
that starts with a small library of abstract adaptation rules and uses
introspective reasoning about the system's memory organization to
generate the memory search plans needed to apply those rules.  The
search plans are then packaged with the original abstract rules to
form new adaptation strategies for future use.  This process allows a
CBR system not only to learn about its domain, by storing the results
of case adaptation, but also to learn how to apply the cases in its
memory more effectively.


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